BUG: dimension mismatch StructuredDotCS{R,C}Grad

dot() called on sparse matrices obeys 2D dimension matching rules, even if they are single row/columns. dot() called on (1, N) and (1, N) arguments, as was being done here, will fail. This transposes the second argument to fix the problem.

BUG: dimension mismatch StructuredDotCS{R,C}Grad
27401cf5 · David Warde-Farley · 247618cc · 27401cf5
--- a/theano/sparse/basic.py
+++ b/theano/sparse/basic.py
@@ -1700,7 +1700,7 @@ class StructuredDotGradCSC(gof.Op):
            ind1 = a_indptr[j + 1]
            for i_idx in xrange(ind0, ind1):
                i = a_indices[i_idx]
-                g_a_data[i_idx] = numpy.dot(g_ab[i], b[j])
+                g_a_data[i_idx] = numpy.dot(g_ab[i], b[j].T)[0, 0]
        out[0] = g_a_data

    def c_code(self, node, name, (_indices, _indptr, _d, _g), (_zout, ), sub):
@@ -1815,7 +1815,7 @@ class StructuredDotGradCSR(gof.Op):
            for j_idx in xrange(ind0, ind1):
                j = a_indices[j_idx]
                # grad is dot product of i-th row of gradient with j-th row of b
-                g_a_data[j_idx] = numpy.dot(g_ab[i], b[j])
+                g_a_data[j_idx] = numpy.dot(g_ab[i], b[j].T)[0, 0]
        out[0] = g_a_data

    def c_code(self, node, name, (_indices, _indptr, _d, _g), (_zout, ), sub):